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Convolutional neural networks for correcting english article errors

  • Chengjie Sun*
  • , Xiaoqiang Jin
  • , Lei Lin
  • , Yuming Zhao
  • , Xiaolong Wang
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper, convolutional neural networks are employed for English article error correction. Instead of employing features relying on human ingenuity and prior natural language processing knowledge, the words surrounding the context of the article are taken as features. Our approach could be trained both on an error annotated corpus and an error non-annotated corpus. Experiments are conducted on CoNLL-2013 data set. Our approach achieves 38.10% in F1, and outperforms the best system (33.40 %) that participates in the task. Experimental results demonstrate the effectiveness of our proposed approach.

Original languageEnglish
Title of host publicationNatural Language Processing and Chinese Computing - 4th CCF Conference, NLPCC 2015, Proceedings
EditorsHeng Ji, Dongyan Zhao, Yansong Feng, Juanzi Li
PublisherSpringer Verlag
Pages102-110
Number of pages9
ISBN (Print)9783319252063
DOIs
StatePublished - 2015
Event4th CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2015 - Nanchang, China
Duration: 9 Oct 201513 Oct 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9362
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference4th CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2015
Country/TerritoryChina
CityNanchang
Period9/10/1513/10/15

Keywords

  • Article error correction
  • Convolutional neural networks
  • Deep learning

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